Title

import pandas as pd
import altair as alt
df = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2018/2018-11-13/malaria_deaths.csv')
#df.head()
df.columns = ['name', 'ISO3166-1-Alpha-3','Year','Death_Rate']
#df['Year'] = pd.to_datetime(df['Year'], format='%Y')

country_info = pd.read_csv("https://raw.githubusercontent.com/datasets/country-codes/master/data/country-codes.csv",dtype = 'str')

df_new = df[df['ISO3166-1-Alpha-3'].isin(country_info['ISO3166-1-Alpha-3'])]
df_new.head()

	name	ISO3166-1-Alpha-3	Year	Death_Rate
0	Afghanistan	AFG	1990	6.802930
1	Afghanistan	AFG	1991	6.973494
2	Afghanistan	AFG	1992	6.989882
3	Afghanistan	AFG	1993	7.088983
4	Afghanistan	AFG	1994	7.392472

df_final = pd.merge(df_new, country_info , on = 'ISO3166-1-Alpha-3', how = 'left')

df_final = df_final[['Year','Death_Rate','ISO3166-1-numeric']]
df_final.head()

	Year	Death_Rate	ISO3166-1-numeric
0	1990	6.802930	004
1	1991	6.973494	004
2	1992	6.989882	004
3	1993	7.088983	004
4	1994	7.392472	004

alt.data_transformers.disable_max_rows()
countries = alt.topo_feature(data.world_110m.url, 'countries')
slider = alt.binding_range(
    step=1,
    min=1990, 
    max=2016
)

select_date = alt.selection_single(
    name="slider", 
    fields=['Year'],
    bind=slider, 
)

from vega_datasets import data
alt.data_transformers.disable_max_rows()
countries = alt.topo_feature(data.world_110m.url, 'countries')
slider = alt.binding_range(
    step=1,
    min=1990, 
    max=2016
)

select_date = alt.selection_single(
    name="slider", 
    fields=['Year'],
    bind=slider, 
)

alt.Chart(df_final).mark_geoshape()\
    .encode(color='Death_Rate:Q')\
    .add_selection(select_date)\
    .transform_filter(select_date)\
    .transform_lookup(
        lookup='ISO3166-1-numeric',
        from_=alt.LookupData(countries, key='id', fields=["type", "properties", "geometry"])
    )\
    .project('orthographic')\
    .properties(
        width=400,
        height=300,
        title='Malaria Death Rate (per 100,000 people) '
    )

df_final['Year'] = pd.to_datetime(df_final['Year'], format='%Y')
country_list = df_final['ISO3166-1-numeric'].dropna().unique()
country_list = country_list.tolist()
dropdown = alt.binding_select(
    options = country_list
)
select_country = alt.selection_single(
    name="dropdown", 
    fields=['ISO3166-1-numeric'],
    bind = dropdown, 
)
alt.Chart(df_final).mark_line().encode(
    x='Year',
    y='Death_Rate',
).add_selection(
   select_country
).transform_filter(select_country)

df1 = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2018/2018-11-13/malaria_inc.csv')
df1.columns = ['name', 'ISO3166-1-Alpha-3','Year','Incidence_Rate']
df1_new = df1[df1['ISO3166-1-Alpha-3'].isin(country_info['ISO3166-1-Alpha-3'])]
df1_new.head()

	name	ISO3166-1-Alpha-3	Year	Incidence_Rate
0	Afghanistan	AFG	2000	107.100000
1	Afghanistan	AFG	2005	46.500000
2	Afghanistan	AFG	2010	23.900000
3	Afghanistan	AFG	2015	23.600000
4	Algeria	DZA	2000	0.037746

df1_final = pd.merge(df1_new, country_info , on = 'ISO3166-1-Alpha-3', how = 'left')
df1_final = df1_final[['Year','Incidence_Rate','ISO3166-1-numeric']]
df1_final.head()

	Year	Incidence_Rate	ISO3166-1-numeric
0	2000	107.100000	004
1	2005	46.500000	004
2	2010	23.900000	004
3	2015	23.600000	004
4	2000	0.037746	012

alt.data_transformers.disable_max_rows()
countries = alt.topo_feature(data.world_110m.url, 'countries')
slider1 = alt.binding_range(
    step=5,
    min=2000, 
    max=2015
)

select_date1 = alt.selection_single(
    name="slider", 
    fields=['Year'],
    bind=slider1, 
)

from vega_datasets import data

alt.Chart(df1_final).mark_geoshape()\
    .encode(color='Incidence_Rate:Q')\
    .add_selection(select_date1)\
    .transform_filter(select_date1)\
    .transform_lookup(
        lookup='ISO3166-1-numeric',
        from_=alt.LookupData(countries, key='id', fields=["type", "properties", "geometry"])
    )\
    .project('orthographic')\
    .properties(
        width=400,
        height=300,
        title='Malaria Incidence Rate (per 1,000 people) '
    )

df2 = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2018/2018-11-13/malaria_deaths_age.csv')
df2.columns = ['index','name', 'ISO3166-1-Alpha-3','Year','Age_Group','Death_Rate']
df2_new = df2[df2['ISO3166-1-Alpha-3'].isin(country_info['ISO3166-1-Alpha-3'])]
df2_new.head()

	index	name	ISO3166-1-Alpha-3	Year	Age_Group	Death_Rate
0	1	Afghanistan	AFG	1990	Under 5	184.606435
1	2	Afghanistan	AFG	1991	Under 5	191.658193
2	3	Afghanistan	AFG	1992	Under 5	197.140197
3	4	Afghanistan	AFG	1993	Under 5	207.357753
4	5	Afghanistan	AFG	1994	Under 5	226.209363

df2_final = pd.merge(df2_new, country_info , on = 'ISO3166-1-Alpha-3', how = 'left')
df2_final = df2_final[['Year','Death_Rate','ISO3166-1-numeric','Age_Group']]
df2_final.head()

	Year	Death_Rate	ISO3166-1-numeric	Age_Group
0	1990	184.606435	004	Under 5
1	1991	191.658193	004	Under 5
2	1992	197.140197	004	Under 5
3	1993	207.357753	004	Under 5
4	1994	226.209363	004	Under 5

df2_final['Year'] = pd.to_datetime(df2_final['Year'], format='%Y')
country_list2 = df2_final['ISO3166-1-numeric'].dropna().unique()
country_list2 = country_list2.tolist()
dropdown2 = alt.binding_select(
    options = country_list2
)
select_country2 = alt.selection_single(
    name="dropdown", 
    fields=['ISO3166-1-numeric'],
    bind = dropdown2, 
)
alt.Chart(df2_final).mark_point().encode(
    x='Year',
    y='Death_Rate',
    color = 'Age_Group'
).add_selection(
   select_country2
).transform_filter(select_country2)